Microwave imaging for breast cancer detection using 3d level set based optimization FDTD method and method of moments

Abstract

Microwave imaging is emerging as new diagnostic option for breast cancer detection because of non-ionizing nature of microwave radiation and significant contrast between dielectric properties of healthy and malignant breast tissues. Class III and IV breasts have more than 50% fibro-glandular tissues. So, it is very difficult to detect cancer in class III and IV breasts by using X-ray based mammography. Microwave imaging is very promising for cancer detection in case of dense breasts. Complex permittivity profile of breasts is reconstructed in three dimensions for microwave breast imaging. 3D level set based optimization proposed in this thesis is able to reconstruct proper shape and dielectric property values of breast tissues. Multiple frequency inverse scattering problem formulation improves computational efficiency and accuracy of microwave imaging system because complex number computations are avoided. Measurements of scattered electric fields are taken at five equally spaced frequencies in the range 0.5-2.5 GHz. Class III numerical breast phantom and Debye model are used in multiple frequency inverse scattering problem formulation. There are three unknowns per cell of numerical breast phantom due to Debye model. Linear relationships between Debye parameters are applied to get only static permittivity as unknown per cell of numerical breast phantom. Two level set functions are used to detect breast cancer in 3D level set based optimization. Pixel based reconstruction is replaced by initial guess about static permittivity solution in this modified four stage reconstruction strategy. Frequency hopping method is used to avoid local minima present at particular frequency in the 3D level set based optimization. 3D FDTD solves forward problem efficiently during each iteration of 3D level set method which leads to better reconstruction of static permittivity profile. newline